Efficient data adaption for musical source separation methods based on parametric models

The decomposition of a monaural audio recording into musically meaningful sound sources constitutes one of the central research topics in music signal processing. In this context, many recent approaches employ parametric models that describe a recording in a highly structured and musically informed way. However, a major drawback of such approaches is that the parameter learning process typically relies on computationally expensive data adaption methods. In this paper, the main idea is to distinguish parameters in which the model is linear explicitly from the remaining parameters. Exploiting the linearity we translate the data adaption problem into a sparse linear least squares problem with box constraints (SLLS-BC), a class of problems for which highly efficient numerical solvers exist. First experiments show that our approach based on modified SLLS-BC methods accelerates the data adaption by a factor of four or more compared to recently proposed methods.